Alpha beta filter

About: Alpha beta filter is a research topic. Over the lifetime, 5653 publications have been published within this topic receiving 128415 citations.

Sensor fusion based on fuzzy Kalman filter

Abstract: We present a fuzzy Kalman filter, which is based on fuzzy logic theory and a Kalman filter. It is similar to a Kalman filter when a linear system with Gaussian noise is considered. However, when non-Gaussian noise is introduced, it is shown that the fuzzy Kalman filter outperforms the Kalman filter and the Kalman filter does not work well. We demonstrate the performances of the Kalman filter and the fuzzy Kalman filter for a position estimation application under different circumstances. Comparisons are made to draw conclusions.

Algorithm for Real Time Correction of Stream Flow Concentration Based on Kalman Filter

Abstract: This paper develops an algorithm for real-time correction of stream flow concentration based on a Kalman filter to improve the performance of real-time forecasting of river discharge under circumstances in which the nonlinearity of stream flow concentration is significant. The Muskingum matrix equation expresses the system of stream flow concentration as a time-varying linear system and satisfies the state-space expression of the Kalman filter. Updating of the parameter matrices of the system impair the influence of the nonlinearity of stream flow concentration on the linear filtering. The advantage of the algorithm is that predictions of every subbasin can be corrected twice by getting “remote” and “local” correction values and can achieve rational updating. Furthermore, to prevent the occurrence of filter divergence and to reach better filtering accuracy, a new real-time statistical method is proposed to estimate the process noise covariance matrix and measurement noise covariance matrix. The algorithm ...

Distributed extended kalman filter based on consensus filter for wireless sensor network

Abstract: Distributed state estimate is one of the most fundamental problems for wireless sensor network. This paper addresses a type of distributed extended kalman filter that is extended from linear distributed kalman filter. Central extended kalman filter is an effective tool for nonlinear state filter of multisensor network. In this paper central extended kalman filter is decomposed into n micro extended kalman filters with inputs that are provided by consensus filters. When system process model and observation model are nonlinear, it is proved that distributed extended kalman filter can provide an identical state estimate of system state. Two target tracking examples are employed for simulation demonstration. All sensor nodes are able to take a nonlinear observation to moving target, dynamical cluster that is composed of several sensor nodes execute observation and error covariance matrix consensus filter. Each sensor in cluster obtain system estimate through distributed extended kalman filter. Simulation results show the proposed algorithm is effective for nonlinear distributed state estimate.

Combining Classification Techniques With Kalman Filters for Aircraft Engine Diagnostics

Abstract: A diagnostic method consisting of a combination of Kalman filters and Bayesian Belief Networks (BBN) is presented. A soft-constrained Kalman filter uses a priori information derived by a BBN at each time step, to derive estimations of the unknown health parameters. The resulting algorithm has improved identification capability in comparison to the stand alone Kalman filter. The paper focuses on the way of combining the information produced by the BBN with the Kalman filter. An extensive set of fault cases is used to test the method on a typical civil turbofan layout. The effectiveness of the method is thus demonstrated and its advantages over individual constituent methods are shown.© 2004 ASME